Quantitative image analysis of protein foam microstructure and its correlation with rheological properties: Egg white foam
José C. Bonilla, Jesper L. Sørensen, Amalie S. Warming, Mathias P. Clausen
Abstract
Recent advances in software development and machine learning algorithms are revolutionizing the way microstructures are analyzed and quantified in fields like biology and neuroscience. With this, comes an upsurge in the opportunities to apply these tools to study food microstructure to gain a deeper understanding of food structure-function relationships. This article shows how a recently developed deep-learning cellular segmentation algorithm, ‘Cellpose’ can be used to identify foam microstructure for further quantification. It successfully identified the air bubbles in a protein foam matrix, from microscopic images captured on a simple brightfield microscope. The segmentation algorithm allowed further quantification of microstructural parameters of the air phase (bubbles) and of the liquid phase (lamella) of the foams. Egg white foams were made with basic ingredients for meringue and the effect of sugar concentration and acidic conditions were studied. Microstructural parameters were analyzed in relation to the rheological responses of the foams. The underlying microstructural mechanisms governing the changes in the foams' stiffness and linearity of the viscoelastic response are shown. Sugar changes bubble size distribution by thickening liquid egg white, it also shortened the linear response to deformation due to its decreasing lamella thickness. Acidity re-shaped the bubbles into more ‘hexagon-like’ structures allowing more air to be incorporated in the foams. The shape of the bubbles under acidic conditions also makes the foams extend their linear response to deformation. The understanding achieved with data from the algorithm-identified microstructures presents a new way to characterize and study food foams.